A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual, Indoor and Outdoor Information Sensing

Abstract

The operating efficiency of heating, ventilation and air conditioning (HVAC) system is critical for building energy performance. Demand-based control is an efficient HVAC operating strategy, which can provide an appropriate level of HVAC services based on the recognition of actual cooling “demand.” The cooling demand primarily relies on the accurate detection of occupancy. The current researches of demand-based HVAC control tend to detect the occupant count using cameras or other sensors, which often impose high computation and costs with limited real-life applications. Instead of detecting the occupant count, this paper proposes to detect the occupancy density. The occupancy density (estimated by image foreground moving pixels) together with the indoor and outdoor information (acquired from existing sensors) are used as inputs to an artificial neural network model for cooling demand estimation. Experiments have been implemented in a university design studio. Results show that, by adding the occupancy density, the cooling demand estimation error is greatly reduced by 67.4% and the R value is improved from 0.75 to 0.96. The proposed approach also features low-cost, computationally efficient, privacy-friendly and easily implementable. It shows good application potentials and can be readily incorporated into existing building management systems for improving energy efficiency.

Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data

“…Yang et al [19] used similar multi-sensor data input of a larger indoor environment and trained a NN (Radial Basis Function, RBF). Wang et al [20] used a similar multi-sensor system for estimating the cooling demand of indoor environments. A relevant interesting review in this regard is the work presented by Ahmad et al [10], which summarizes the most commonly used ambient indoor sensors in multi-sensor occupancy prediction applications.…”

Metabolism-based ventilation monitoring and control method for COVID-19 risk mitigation in gymnasiums and alike places

“…Computer vision simulates the human visual system by analyzing the rich information provided by digital images or video to achieve a high level of understanding of objects and events presented in a scene ( Guo et al, 2021 ). Using traditional surveillance cameras, computer vision technology can obtain information about occupants at a low cost, such as occupant number ( Wang et al, 2019 ), density ( Sun et al, 2022 ; Wang et al, 2018 ), location ( Huang & Hao, 2020 ), and posture ( Chun et al, 2015 ). This paper combines computer vision techniques and artificial neural network to predict metabolic rates which are used as the input signal for ventilation control to reduce the risk of infection.…”

Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand

“…It can provide information about the presence, count and activity of the occupants in the space, enabling better control and flexible management of HVAC. Such detailed information can help predict how much heat, CO2, and contaminants are produced by the occupants and how they interact with appliances and lighting, producing heat and windows and openings that affect the air and heat exchange [32].…”